Background　High-risk　chest　pain　is　a　critical　presentation　in　emergency　departments,　frequently　indicative　of　life-threatening　cardiopulmonary　conditions.　Rapid　and　accurate　diagnosis　is　pivotal　for　improving　patient　survival　rates.Methods　We　developed　a　machine　learning　prediction　model　using　the　MIMIC-IV　database　(n　=　14,716　patients,　including　1,302　high-risk　cases).　To　address　class　imbalance,　we　implemented　feature　engineering　with　SMOTE　and　under-sampling　techniques.　Model　optimization　was　performed　via　Bayesian　hyperparameter　tuning.　Seven　algorithms　were　evaluated:　Logistic　Regression,　Random　Forest,　SVM,　XGBoost,　LightGBM,　TabTransformer,　and　TabNet.Results　The　LightGBM　model　demonstrated　superior　performance　with　accuracy　=　0.95,　precision　=　0.95,　recall　=　0.95,　and　F1-score　=　0.94.　SHAP　analysis　revealed　maximum　troponin　and　creatine　kinase-MB　levels　as　the　top　predictive　features.Conclusion　Our　optimized　LightGBM　model　provides　clinically　significant　predictive　capability　for　high-risk　chest　pain,　offering　emergency　physicians　a　decision-support　tool　to　enhance　diagnostic　accuracy　and　patient　outcomes.